neural_nets_intro.ipynb

    "plt.ylim([-2, 2])\n",
    "plt.title(\"Blue points are False\")\n",
    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\") ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a simple Keras model\n",
    "\n",
    "def a_simple_NN():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(4, activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 350 samples, validate on 150 samples\n",
      "Epoch 1/300\n",
      "350/350 [==============================] - 1s 3ms/step - loss: 0.7545 - acc: 0.4143 - val_loss: 0.7368 - val_acc: 0.4200\n",
      "Epoch 2/300\n",
      "350/350 [==============================] - 0s 74us/step - loss: 0.7379 - acc: 0.4029 - val_loss: 0.7255 - val_acc: 0.4467\n",
      "Epoch 3/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.7260 - acc: 0.4000 - val_loss: 0.7164 - val_acc: 0.4400\n",
      "Epoch 4/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.7157 - acc: 0.3857 - val_loss: 0.7084 - val_acc: 0.4533\n",
      "Epoch 5/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.7065 - acc: 0.3914 - val_loss: 0.7010 - val_acc: 0.4467\n",
      "Epoch 6/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.6976 - acc: 0.3914 - val_loss: 0.6935 - val_acc: 0.4400\n",
      "Epoch 7/300\n",
      "350/350 [==============================] - 0s 72us/step - loss: 0.6890 - acc: 0.3914 - val_loss: 0.6867 - val_acc: 0.4267\n",
      "Epoch 8/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.6810 - acc: 0.3971 - val_loss: 0.6803 - val_acc: 0.4267\n",
      "Epoch 9/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.6737 - acc: 0.3971 - val_loss: 0.6745 - val_acc: 0.4333\n",
      "Epoch 10/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.6666 - acc: 0.4143 - val_loss: 0.6689 - val_acc: 0.4467\n",
      "Epoch 11/300\n",
      "350/350 [==============================] - 0s 102us/step - loss: 0.6601 - acc: 0.4314 - val_loss: 0.6639 - val_acc: 0.4400\n",
      "Epoch 12/300\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.6541 - acc: 0.4400 - val_loss: 0.6591 - val_acc: 0.4933\n",
      "Epoch 13/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.6482 - acc: 0.4800 - val_loss: 0.6542 - val_acc: 0.5667\n",
      "Epoch 14/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.6427 - acc: 0.5657 - val_loss: 0.6497 - val_acc: 0.5867\n",
      "Epoch 15/300\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.6377 - acc: 0.5886 - val_loss: 0.6456 - val_acc: 0.6000\n",
      "Epoch 16/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.6330 - acc: 0.6086 - val_loss: 0.6419 - val_acc: 0.6200\n",
      "Epoch 17/300\n",
      "350/350 [==============================] - 0s 118us/step - loss: 0.6285 - acc: 0.6200 - val_loss: 0.6382 - val_acc: 0.6333\n",
      "Epoch 18/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.6240 - acc: 0.6229 - val_loss: 0.6346 - val_acc: 0.6333\n",
      "Epoch 19/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.6196 - acc: 0.6257 - val_loss: 0.6312 - val_acc: 0.6333\n",
      "Epoch 20/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.6155 - acc: 0.6314 - val_loss: 0.6279 - val_acc: 0.6333\n",
      "Epoch 21/300\n",
      "350/350 [==============================] - 0s 113us/step - loss: 0.6113 - acc: 0.6343 - val_loss: 0.6245 - val_acc: 0.6333\n",
      "Epoch 22/300\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.6071 - acc: 0.6371 - val_loss: 0.6211 - val_acc: 0.6400\n",
      "Epoch 23/300\n",
      "350/350 [==============================] - 0s 114us/step - loss: 0.6033 - acc: 0.6371 - val_loss: 0.6180 - val_acc: 0.6533\n",
      "Epoch 24/300\n",
      "350/350 [==============================] - 0s 111us/step - loss: 0.5994 - acc: 0.6400 - val_loss: 0.6148 - val_acc: 0.6533\n",
      "Epoch 25/300\n",
      "350/350 [==============================] - 0s 112us/step - loss: 0.5958 - acc: 0.6400 - val_loss: 0.6119 - val_acc: 0.6533\n",
      "Epoch 26/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.5923 - acc: 0.6400 - val_loss: 0.6089 - val_acc: 0.6600\n",
      "Epoch 27/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.5889 - acc: 0.6429 - val_loss: 0.6061 - val_acc: 0.6600\n",
      "Epoch 28/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.5855 - acc: 0.6486 - val_loss: 0.6033 - val_acc: 0.6600\n",
      "Epoch 29/300\n",
      "350/350 [==============================] - 0s 112us/step - loss: 0.5821 - acc: 0.6543 - val_loss: 0.6004 - val_acc: 0.6600\n",
      "Epoch 30/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.5787 - acc: 0.6543 - val_loss: 0.5975 - val_acc: 0.6600\n",
      "Epoch 31/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.5752 - acc: 0.6543 - val_loss: 0.5948 - val_acc: 0.6667\n",
      "Epoch 32/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.5717 - acc: 0.6571 - val_loss: 0.5920 - val_acc: 0.6733\n",
      "Epoch 33/300\n",
      "350/350 [==============================] - 0s 113us/step - loss: 0.5685 - acc: 0.6600 - val_loss: 0.5894 - val_acc: 0.6800\n",
      "Epoch 34/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.5654 - acc: 0.6629 - val_loss: 0.5869 - val_acc: 0.6733\n",
      "Epoch 35/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.5625 - acc: 0.6629 - val_loss: 0.5846 - val_acc: 0.6733\n",
      "Epoch 36/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.5595 - acc: 0.6629 - val_loss: 0.5822 - val_acc: 0.6600\n",
      "Epoch 37/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.5565 - acc: 0.6629 - val_loss: 0.5797 - val_acc: 0.6600\n",
      "Epoch 38/300\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.5537 - acc: 0.6629 - val_loss: 0.5774 - val_acc: 0.6600\n",
      "Epoch 39/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.5506 - acc: 0.6629 - val_loss: 0.5748 - val_acc: 0.6600\n",
      "Epoch 40/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.5480 - acc: 0.6714 - val_loss: 0.5725 - val_acc: 0.6667\n",
      "Epoch 41/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.5453 - acc: 0.6743 - val_loss: 0.5703 - val_acc: 0.6667\n",
      "Epoch 42/300\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.5424 - acc: 0.6743 - val_loss: 0.5679 - val_acc: 0.6667\n",
      "Epoch 43/300\n",
      "350/350 [==============================] - 0s 109us/step - loss: 0.5394 - acc: 0.6771 - val_loss: 0.5654 - val_acc: 0.6667\n",
      "Epoch 44/300\n",
      "350/350 [==============================] - 0s 117us/step - loss: 0.5365 - acc: 0.6771 - val_loss: 0.5628 - val_acc: 0.6667\n",
      "Epoch 45/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.5335 - acc: 0.6771 - val_loss: 0.5603 - val_acc: 0.6667\n",
      "Epoch 46/300\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.5305 - acc: 0.6743 - val_loss: 0.5577 - val_acc: 0.6667\n",
      "Epoch 47/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.5274 - acc: 0.6743 - val_loss: 0.5551 - val_acc: 0.6667\n",
      "Epoch 48/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.5244 - acc: 0.6743 - val_loss: 0.5525 - val_acc: 0.6733\n",
      "Epoch 49/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.5213 - acc: 0.6771 - val_loss: 0.5496 - val_acc: 0.6733\n",
      "Epoch 50/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.5180 - acc: 0.6771 - val_loss: 0.5468 - val_acc: 0.6733\n",
      "Epoch 51/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.5148 - acc: 0.6771 - val_loss: 0.5439 - val_acc: 0.6733\n",
      "Epoch 52/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.5115 - acc: 0.6771 - val_loss: 0.5408 - val_acc: 0.6733\n",
      "Epoch 53/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.5081 - acc: 0.6771 - val_loss: 0.5377 - val_acc: 0.6733\n",
      "Epoch 54/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.5044 - acc: 0.6800 - val_loss: 0.5342 - val_acc: 0.6733\n",
      "Epoch 55/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.5005 - acc: 0.6800 - val_loss: 0.5304 - val_acc: 0.6733\n",
      "Epoch 56/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.4965 - acc: 0.6800 - val_loss: 0.5266 - val_acc: 0.6733\n",
      "Epoch 57/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.4925 - acc: 0.6829 - val_loss: 0.5227 - val_acc: 0.6733\n",
      "Epoch 58/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.4882 - acc: 0.6829 - val_loss: 0.5187 - val_acc: 0.6733\n",
      "Epoch 59/300\n",
      "350/350 [==============================] - 0s 80us/step - loss: 0.4841 - acc: 0.6857 - val_loss: 0.5147 - val_acc: 0.6733\n",
      "Epoch 60/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.4798 - acc: 0.6829 - val_loss: 0.5103 - val_acc: 0.6733\n",
      "Epoch 61/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 96us/step - loss: 0.4753 - acc: 0.6886 - val_loss: 0.5059 - val_acc: 0.6733\n",
      "Epoch 62/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.4710 - acc: 0.6857 - val_loss: 0.5016 - val_acc: 0.6733\n",
      "Epoch 63/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.4664 - acc: 0.6914 - val_loss: 0.4973 - val_acc: 0.6733\n",
      "Epoch 64/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.4620 - acc: 0.6886 - val_loss: 0.4930 - val_acc: 0.6733\n",
      "Epoch 65/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.4575 - acc: 0.6886 - val_loss: 0.4882 - val_acc: 0.6733\n",
      "Epoch 66/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.4527 - acc: 0.6829 - val_loss: 0.4835 - val_acc: 0.6733\n",
      "Epoch 67/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.4481 - acc: 0.6886 - val_loss: 0.4788 - val_acc: 0.6733\n",
      "Epoch 68/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.4435 - acc: 0.7543 - val_loss: 0.4742 - val_acc: 0.7933\n",
      "Epoch 69/300\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.4391 - acc: 0.8114 - val_loss: 0.4698 - val_acc: 0.8000\n",
      "Epoch 70/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.4348 - acc: 0.8143 - val_loss: 0.4654 - val_acc: 0.8133\n",
      "Epoch 71/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.4305 - acc: 0.8171 - val_loss: 0.4609 - val_acc: 0.8133\n",
      "Epoch 72/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.4263 - acc: 0.8229 - val_loss: 0.4565 - val_acc: 0.8267\n",
      "Epoch 73/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.4223 - acc: 0.8286 - val_loss: 0.4525 - val_acc: 0.8333\n",
      "Epoch 74/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.4183 - acc: 0.8371 - val_loss: 0.4484 - val_acc: 0.8400\n",
      "Epoch 75/300\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.4144 - acc: 0.8371 - val_loss: 0.4444 - val_acc: 0.8533\n",
      "Epoch 76/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.4109 - acc: 0.8400 - val_loss: 0.4404 - val_acc: 0.8533\n",
      "Epoch 77/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.4072 - acc: 0.8343 - val_loss: 0.4366 - val_acc: 0.8600\n",
      "Epoch 78/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.4037 - acc: 0.8457 - val_loss: 0.4328 - val_acc: 0.8600\n",
      "Epoch 79/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.4000 - acc: 0.8514 - val_loss: 0.4289 - val_acc: 0.8600\n",
      "Epoch 80/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.3963 - acc: 0.8629 - val_loss: 0.4251 - val_acc: 0.8667\n",
      "Epoch 81/300\n",
      "350/350 [==============================] - 0s 80us/step - loss: 0.3931 - acc: 0.8714 - val_loss: 0.4216 - val_acc: 0.8667\n",
      "Epoch 82/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.3896 - acc: 0.8714 - val_loss: 0.4181 - val_acc: 0.8667\n",
      "Epoch 83/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.3862 - acc: 0.8714 - val_loss: 0.4145 - val_acc: 0.8667\n",
      "Epoch 84/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.3829 - acc: 0.8714 - val_loss: 0.4108 - val_acc: 0.8667\n",
      "Epoch 85/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.3792 - acc: 0.8743 - val_loss: 0.4070 - val_acc: 0.8667\n",
      "Epoch 86/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.3758 - acc: 0.8686 - val_loss: 0.4031 - val_acc: 0.8867\n",
      "Epoch 87/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.3723 - acc: 0.8686 - val_loss: 0.3993 - val_acc: 0.8867\n",
      "Epoch 88/300\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.3693 - acc: 0.8743 - val_loss: 0.3959 - val_acc: 0.8867\n",
      "Epoch 89/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.3660 - acc: 0.8771 - val_loss: 0.3926 - val_acc: 0.8867\n",
      "Epoch 90/300\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.3631 - acc: 0.8857 - val_loss: 0.3896 - val_acc: 0.8800\n",
      "Epoch 91/300\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.3602 - acc: 0.8943 - val_loss: 0.3863 - val_acc: 0.8800\n",
      "Epoch 92/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.3573 - acc: 0.8943 - val_loss: 0.3831 - val_acc: 0.8867\n",
      "Epoch 93/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.3544 - acc: 0.8943 - val_loss: 0.3799 - val_acc: 0.8867\n",
      "Epoch 94/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.3513 - acc: 0.8886 - val_loss: 0.3767 - val_acc: 0.8867\n",
      "Epoch 95/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.3484 - acc: 0.8971 - val_loss: 0.3735 - val_acc: 0.8867\n",
      "Epoch 96/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.3456 - acc: 0.8971 - val_loss: 0.3704 - val_acc: 0.8867\n",
      "Epoch 97/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.3430 - acc: 0.8971 - val_loss: 0.3675 - val_acc: 0.8867\n",
      "Epoch 98/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.3400 - acc: 0.8971 - val_loss: 0.3645 - val_acc: 0.8867\n",
      "Epoch 99/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.3372 - acc: 0.9029 - val_loss: 0.3614 - val_acc: 0.9000\n",
      "Epoch 100/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.3343 - acc: 0.9029 - val_loss: 0.3586 - val_acc: 0.9000\n",
      "Epoch 101/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.3316 - acc: 0.9029 - val_loss: 0.3556 - val_acc: 0.9067\n",
      "Epoch 102/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.3287 - acc: 0.9029 - val_loss: 0.3528 - val_acc: 0.9067\n",
      "Epoch 103/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.3261 - acc: 0.9086 - val_loss: 0.3501 - val_acc: 0.9133\n",
      "Epoch 104/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.3231 - acc: 0.9086 - val_loss: 0.3471 - val_acc: 0.9133\n",
      "Epoch 105/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.3206 - acc: 0.9057 - val_loss: 0.3444 - val_acc: 0.9067\n",
      "Epoch 106/300\n",
      "350/350 [==============================] - 0s 102us/step - loss: 0.3182 - acc: 0.9086 - val_loss: 0.3418 - val_acc: 0.9133\n",
      "Epoch 107/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.3156 - acc: 0.9086 - val_loss: 0.3392 - val_acc: 0.9133\n",
      "Epoch 108/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.3133 - acc: 0.9086 - val_loss: 0.3369 - val_acc: 0.9133\n",
      "Epoch 109/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.3107 - acc: 0.9086 - val_loss: 0.3343 - val_acc: 0.9133\n",
      "Epoch 110/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.3085 - acc: 0.9057 - val_loss: 0.3317 - val_acc: 0.9133\n",
      "Epoch 111/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.3058 - acc: 0.9057 - val_loss: 0.3292 - val_acc: 0.9133\n",
      "Epoch 112/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.3038 - acc: 0.9057 - val_loss: 0.3267 - val_acc: 0.9133\n",
      "Epoch 113/300\n",
      "350/350 [==============================] - 0s 111us/step - loss: 0.3011 - acc: 0.9057 - val_loss: 0.3245 - val_acc: 0.9133\n",
      "Epoch 114/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.2991 - acc: 0.9057 - val_loss: 0.3222 - val_acc: 0.9133\n",
      "Epoch 115/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.2967 - acc: 0.9057 - val_loss: 0.3197 - val_acc: 0.9133\n",
      "Epoch 116/300\n",
      "350/350 [==============================] - 0s 112us/step - loss: 0.2942 - acc: 0.9057 - val_loss: 0.3173 - val_acc: 0.9133\n",
      "Epoch 117/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.2922 - acc: 0.9086 - val_loss: 0.3149 - val_acc: 0.9133\n",
      "Epoch 118/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.2899 - acc: 0.9114 - val_loss: 0.3129 - val_acc: 0.9133\n",
      "Epoch 119/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.2876 - acc: 0.9143 - val_loss: 0.3105 - val_acc: 0.9200\n",
      "Epoch 120/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.2858 - acc: 0.9114 - val_loss: 0.3082 - val_acc: 0.9200\n",
      "Epoch 121/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 90us/step - loss: 0.2838 - acc: 0.9171 - val_loss: 0.3063 - val_acc: 0.9133\n",
      "Epoch 122/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.2818 - acc: 0.9114 - val_loss: 0.3043 - val_acc: 0.9133\n",
      "Epoch 123/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.2798 - acc: 0.9143 - val_loss: 0.3022 - val_acc: 0.9133\n",
      "Epoch 124/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.2779 - acc: 0.9143 - val_loss: 0.3000 - val_acc: 0.9133\n",
      "Epoch 125/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.2758 - acc: 0.9171 - val_loss: 0.2978 - val_acc: 0.9133\n",
      "Epoch 126/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.2739 - acc: 0.9200 - val_loss: 0.2958 - val_acc: 0.9133\n",
      "Epoch 127/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.2721 - acc: 0.9200 - val_loss: 0.2939 - val_acc: 0.9133\n",
      "Epoch 128/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.2700 - acc: 0.9229 - val_loss: 0.2918 - val_acc: 0.9200\n",
      "Epoch 129/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.2681 - acc: 0.9286 - val_loss: 0.2899 - val_acc: 0.9200\n",
      "Epoch 130/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.2663 - acc: 0.9229 - val_loss: 0.2879 - val_acc: 0.9200\n",
      "Epoch 131/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.2643 - acc: 0.9314 - val_loss: 0.2858 - val_acc: 0.9200\n",
      "Epoch 132/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.2625 - acc: 0.9257 - val_loss: 0.2838 - val_acc: 0.9200\n",
      "Epoch 133/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.2604 - acc: 0.9286 - val_loss: 0.2820 - val_acc: 0.9200\n",
      "Epoch 134/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.2587 - acc: 0.9286 - val_loss: 0.2802 - val_acc: 0.9200\n",
      "Epoch 135/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.2570 - acc: 0.9286 - val_loss: 0.2784 - val_acc: 0.9200\n",
      "Epoch 136/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.2551 - acc: 0.9314 - val_loss: 0.2766 - val_acc: 0.9200\n",
      "Epoch 137/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.2535 - acc: 0.9343 - val_loss: 0.2748 - val_acc: 0.9200\n",
      "Epoch 138/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.2516 - acc: 0.9400 - val_loss: 0.2732 - val_acc: 0.9200\n",
      "Epoch 139/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.2500 - acc: 0.9400 - val_loss: 0.2716 - val_acc: 0.9200\n",
      "Epoch 140/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.2485 - acc: 0.9400 - val_loss: 0.2704 - val_acc: 0.9200\n",
      "Epoch 141/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.2468 - acc: 0.9400 - val_loss: 0.2686 - val_acc: 0.9200\n",
      "Epoch 142/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.2455 - acc: 0.9429 - val_loss: 0.2671 - val_acc: 0.9200\n",
      "Epoch 143/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.2436 - acc: 0.9429 - val_loss: 0.2654 - val_acc: 0.9200\n",
      "Epoch 144/300\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.2419 - acc: 0.9429 - val_loss: 0.2637 - val_acc: 0.9200\n",
      "Epoch 145/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.2400 - acc: 0.9429 - val_loss: 0.2620 - val_acc: 0.9200\n",
      "Epoch 146/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.2386 - acc: 0.9429 - val_loss: 0.2604 - val_acc: 0.9200\n",
      "Epoch 147/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.2368 - acc: 0.9429 - val_loss: 0.2589 - val_acc: 0.9200\n",
      "Epoch 148/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.2354 - acc: 0.9429 - val_loss: 0.2573 - val_acc: 0.9200\n",
      "Epoch 149/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.2336 - acc: 0.9429 - val_loss: 0.2559 - val_acc: 0.9200\n",
      "Epoch 150/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.2321 - acc: 0.9429 - val_loss: 0.2545 - val_acc: 0.9200\n",
      "Epoch 151/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.2305 - acc: 0.9429 - val_loss: 0.2531 - val_acc: 0.9200\n",
      "Epoch 152/300\n",
      "350/350 [==============================] - ETA: 0s - loss: 0.2170 - acc: 0.906 - 0s 97us/step - loss: 0.2293 - acc: 0.9429 - val_loss: 0.2518 - val_acc: 0.9267\n",
      "Epoch 153/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.2277 - acc: 0.9429 - val_loss: 0.2506 - val_acc: 0.9267\n",
      "Epoch 154/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.2264 - acc: 0.9457 - val_loss: 0.2491 - val_acc: 0.9267\n",
      "Epoch 155/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.2250 - acc: 0.9457 - val_loss: 0.2479 - val_acc: 0.9267\n",
      "Epoch 156/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.2235 - acc: 0.9457 - val_loss: 0.2467 - val_acc: 0.9267\n",
      "Epoch 157/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.2224 - acc: 0.9457 - val_loss: 0.2454 - val_acc: 0.9333\n",
      "Epoch 158/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.2211 - acc: 0.9457 - val_loss: 0.2440 - val_acc: 0.9333\n",
      "Epoch 159/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.2197 - acc: 0.9457 - val_loss: 0.2429 - val_acc: 0.9333\n",
      "Epoch 160/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.2183 - acc: 0.9457 - val_loss: 0.2414 - val_acc: 0.9333\n",
      "Epoch 161/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.2169 - acc: 0.9457 - val_loss: 0.2400 - val_acc: 0.9333\n",
      "Epoch 162/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.2155 - acc: 0.9457 - val_loss: 0.2388 - val_acc: 0.9400\n",
      "Epoch 163/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.2141 - acc: 0.9486 - val_loss: 0.2374 - val_acc: 0.9333\n",
      "Epoch 164/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.2127 - acc: 0.9486 - val_loss: 0.2363 - val_acc: 0.9333\n",
      "Epoch 165/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.2115 - acc: 0.9457 - val_loss: 0.2350 - val_acc: 0.9333\n",
      "Epoch 166/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.2102 - acc: 0.9457 - val_loss: 0.2338 - val_acc: 0.9333\n",
      "Epoch 167/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.2090 - acc: 0.9486 - val_loss: 0.2328 - val_acc: 0.9333\n",
      "Epoch 168/300\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.2077 - acc: 0.9486 - val_loss: 0.2316 - val_acc: 0.9333\n",
      "Epoch 169/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.2065 - acc: 0.9486 - val_loss: 0.2302 - val_acc: 0.9333\n",
      "Epoch 170/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.2054 - acc: 0.9486 - val_loss: 0.2291 - val_acc: 0.9400\n",
      "Epoch 171/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.2040 - acc: 0.9486 - val_loss: 0.2281 - val_acc: 0.9400\n",
      "Epoch 172/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.2030 - acc: 0.9514 - val_loss: 0.2271 - val_acc: 0.9400\n",
      "Epoch 173/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.2017 - acc: 0.9514 - val_loss: 0.2263 - val_acc: 0.9333\n",
      "Epoch 174/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.2006 - acc: 0.9486 - val_loss: 0.2250 - val_acc: 0.9333\n",
      "Epoch 175/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1994 - acc: 0.9514 - val_loss: 0.2239 - val_acc: 0.9400\n",
      "Epoch 176/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1982 - acc: 0.9514 - val_loss: 0.2229 - val_acc: 0.9400\n",
      "Epoch 177/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.1969 - acc: 0.9514 - val_loss: 0.2215 - val_acc: 0.9400\n",
      "Epoch 178/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.1956 - acc: 0.9514 - val_loss: 0.2205 - val_acc: 0.9467\n",
      "Epoch 179/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1947 - acc: 0.9514 - val_loss: 0.2193 - val_acc: 0.9467\n",
      "Epoch 180/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1934 - acc: 0.9514 - val_loss: 0.2184 - val_acc: 0.9467\n",
      "Epoch 181/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 91us/step - loss: 0.1922 - acc: 0.9514 - val_loss: 0.2174 - val_acc: 0.9467\n",
      "Epoch 182/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.1910 - acc: 0.9514 - val_loss: 0.2162 - val_acc: 0.9467\n",
      "Epoch 183/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.1897 - acc: 0.9571 - val_loss: 0.2149 - val_acc: 0.9467\n",
      "Epoch 184/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.1885 - acc: 0.9571 - val_loss: 0.2135 - val_acc: 0.9467\n",
      "Epoch 185/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1877 - acc: 0.9543 - val_loss: 0.2124 - val_acc: 0.9467\n",
      "Epoch 186/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1865 - acc: 0.9571 - val_loss: 0.2115 - val_acc: 0.9467\n",
      "Epoch 187/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1852 - acc: 0.9543 - val_loss: 0.2104 - val_acc: 0.9467\n",
      "Epoch 188/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1844 - acc: 0.9571 - val_loss: 0.2096 - val_acc: 0.9467\n",
      "Epoch 189/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1831 - acc: 0.9571 - val_loss: 0.2088 - val_acc: 0.9467\n",
      "Epoch 190/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1819 - acc: 0.9600 - val_loss: 0.2077 - val_acc: 0.9467\n",
      "Epoch 191/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1809 - acc: 0.9571 - val_loss: 0.2066 - val_acc: 0.9467\n",
      "Epoch 192/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1803 - acc: 0.9600 - val_loss: 0.2060 - val_acc: 0.9467\n",
      "Epoch 193/300\n",
      "350/350 [==============================] - 0s 102us/step - loss: 0.1790 - acc: 0.9600 - val_loss: 0.2053 - val_acc: 0.9467\n",
      "Epoch 194/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1781 - acc: 0.9600 - val_loss: 0.2044 - val_acc: 0.9467\n",
      "Epoch 195/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1771 - acc: 0.9600 - val_loss: 0.2035 - val_acc: 0.9467\n",
      "Epoch 196/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1764 - acc: 0.9600 - val_loss: 0.2026 - val_acc: 0.9467\n",
      "Epoch 197/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1754 - acc: 0.9600 - val_loss: 0.2018 - val_acc: 0.9467\n",
      "Epoch 198/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1743 - acc: 0.9600 - val_loss: 0.2007 - val_acc: 0.9467\n",
      "Epoch 199/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1734 - acc: 0.9629 - val_loss: 0.1999 - val_acc: 0.9467\n",
      "Epoch 200/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1723 - acc: 0.9629 - val_loss: 0.1996 - val_acc: 0.9467\n",
      "Epoch 201/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1716 - acc: 0.9657 - val_loss: 0.1985 - val_acc: 0.9467\n",
      "Epoch 202/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.1705 - acc: 0.9686 - val_loss: 0.1977 - val_acc: 0.9533\n",
      "Epoch 203/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1698 - acc: 0.9657 - val_loss: 0.1967 - val_acc: 0.9533\n",
      "Epoch 204/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1685 - acc: 0.9686 - val_loss: 0.1961 - val_acc: 0.9533\n",
      "Epoch 205/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.1676 - acc: 0.9686 - val_loss: 0.1948 - val_acc: 0.9533\n",
      "Epoch 206/300\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.1667 - acc: 0.9686 - val_loss: 0.1943 - val_acc: 0.9533\n",
      "Epoch 207/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1657 - acc: 0.9686 - val_loss: 0.1934 - val_acc: 0.9533\n",
      "Epoch 208/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.1649 - acc: 0.9686 - val_loss: 0.1926 - val_acc: 0.9533\n",
      "Epoch 209/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.1640 - acc: 0.9686 - val_loss: 0.1920 - val_acc: 0.9533\n",
      "Epoch 210/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.1629 - acc: 0.9714 - val_loss: 0.1912 - val_acc: 0.9533\n",
      "Epoch 211/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1622 - acc: 0.9686 - val_loss: 0.1905 - val_acc: 0.9533\n",
      "Epoch 212/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1612 - acc: 0.9743 - val_loss: 0.1896 - val_acc: 0.9533\n",
      "Epoch 213/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.1601 - acc: 0.9743 - val_loss: 0.1888 - val_acc: 0.9533\n",
      "Epoch 214/300\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.1595 - acc: 0.9743 - val_loss: 0.1878 - val_acc: 0.9533\n",
      "Epoch 215/300\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.1585 - acc: 0.9743 - val_loss: 0.1874 - val_acc: 0.9533\n",
      "Epoch 216/300\n",
      "350/350 [==============================] - 0s 122us/step - loss: 0.1578 - acc: 0.9743 - val_loss: 0.1867 - val_acc: 0.9533\n",
      "Epoch 217/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.1568 - acc: 0.9743 - val_loss: 0.1860 - val_acc: 0.9533\n",
      "Epoch 218/300\n",
      "350/350 [==============================] - 0s 105us/step - loss: 0.1561 - acc: 0.9743 - val_loss: 0.1853 - val_acc: 0.9533\n",
      "Epoch 219/300\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.1553 - acc: 0.9743 - val_loss: 0.1844 - val_acc: 0.9533\n",
      "Epoch 220/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.1544 - acc: 0.9743 - val_loss: 0.1838 - val_acc: 0.9533\n",
      "Epoch 221/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1538 - acc: 0.9743 - val_loss: 0.1830 - val_acc: 0.9533\n",
      "Epoch 222/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1530 - acc: 0.9714 - val_loss: 0.1825 - val_acc: 0.9533\n",
      "Epoch 223/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1524 - acc: 0.9743 - val_loss: 0.1818 - val_acc: 0.9533\n",
      "Epoch 224/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1514 - acc: 0.9743 - val_loss: 0.1811 - val_acc: 0.9533\n",
      "Epoch 225/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1510 - acc: 0.9743 - val_loss: 0.1804 - val_acc: 0.9533\n",
      "Epoch 226/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1500 - acc: 0.9743 - val_loss: 0.1797 - val_acc: 0.9533\n",
      "Epoch 227/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.1496 - acc: 0.9743 - val_loss: 0.1793 - val_acc: 0.9533\n",
      "Epoch 228/300\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.1487 - acc: 0.9743 - val_loss: 0.1786 - val_acc: 0.9533\n",
      "Epoch 229/300\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.1481 - acc: 0.9743 - val_loss: 0.1782 - val_acc: 0.9533\n",
      "Epoch 230/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1475 - acc: 0.9743 - val_loss: 0.1774 - val_acc: 0.9533\n",
      "Epoch 231/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.1467 - acc: 0.9743 - val_loss: 0.1765 - val_acc: 0.9533\n",
      "Epoch 232/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1461 - acc: 0.9743 - val_loss: 0.1758 - val_acc: 0.9533\n",
      "Epoch 233/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1455 - acc: 0.9743 - val_loss: 0.1751 - val_acc: 0.9533\n",
      "Epoch 234/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.1447 - acc: 0.9743 - val_loss: 0.1748 - val_acc: 0.9533\n",
      "Epoch 235/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1443 - acc: 0.9743 - val_loss: 0.1744 - val_acc: 0.9533\n",
      "Epoch 236/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1436 - acc: 0.9743 - val_loss: 0.1737 - val_acc: 0.9533\n",
      "Epoch 237/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.1431 - acc: 0.9743 - val_loss: 0.1732 - val_acc: 0.9533\n",
      "Epoch 238/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1423 - acc: 0.9743 - val_loss: 0.1725 - val_acc: 0.9533\n",
      "Epoch 239/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1418 - acc: 0.9714 - val_loss: 0.1724 - val_acc: 0.9533\n",
      "Epoch 240/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1411 - acc: 0.9743 - val_loss: 0.1723 - val_acc: 0.9533\n",
      "Epoch 241/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 114us/step - loss: 0.1404 - acc: 0.9743 - val_loss: 0.1717 - val_acc: 0.9533\n",
      "Epoch 242/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1398 - acc: 0.9743 - val_loss: 0.1710 - val_acc: 0.9533\n",
      "Epoch 243/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.1396 - acc: 0.9714 - val_loss: 0.1705 - val_acc: 0.9533\n",
      "Epoch 244/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1387 - acc: 0.9743 - val_loss: 0.1699 - val_acc: 0.9533\n",
      "Epoch 245/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1383 - acc: 0.9714 - val_loss: 0.1697 - val_acc: 0.9533\n",
      "Epoch 246/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1373 - acc: 0.9743 - val_loss: 0.1692 - val_acc: 0.9533\n",
      "Epoch 247/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1371 - acc: 0.9771 - val_loss: 0.1683 - val_acc: 0.9533\n",
      "Epoch 248/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1363 - acc: 0.9743 - val_loss: 0.1676 - val_acc: 0.9533\n",
      "Epoch 249/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1357 - acc: 0.9771 - val_loss: 0.1674 - val_acc: 0.9533\n",
      "Epoch 250/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1353 - acc: 0.9743 - val_loss: 0.1670 - val_acc: 0.9533\n",
      "Epoch 251/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1346 - acc: 0.9743 - val_loss: 0.1667 - val_acc: 0.9533\n",
      "Epoch 252/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.1341 - acc: 0.9743 - val_loss: 0.1663 - val_acc: 0.9533\n",
      "Epoch 253/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1337 - acc: 0.9743 - val_loss: 0.1659 - val_acc: 0.9533\n",
      "Epoch 254/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1328 - acc: 0.9771 - val_loss: 0.1651 - val_acc: 0.9533\n",
      "Epoch 255/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1324 - acc: 0.9743 - val_loss: 0.1649 - val_acc: 0.9533\n",
      "Epoch 256/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1321 - acc: 0.9771 - val_loss: 0.1644 - val_acc: 0.9533\n",
      "Epoch 257/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1315 - acc: 0.9771 - val_loss: 0.1641 - val_acc: 0.9533\n",
      "Epoch 258/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1308 - acc: 0.9771 - val_loss: 0.1638 - val_acc: 0.9533\n",
      "Epoch 259/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.1303 - acc: 0.9771 - val_loss: 0.1635 - val_acc: 0.9533\n",
      "Epoch 260/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1298 - acc: 0.9771 - val_loss: 0.1626 - val_acc: 0.9533\n",
      "Epoch 261/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1292 - acc: 0.9743 - val_loss: 0.1625 - val_acc: 0.9533\n",
      "Epoch 262/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1287 - acc: 0.9771 - val_loss: 0.1620 - val_acc: 0.9533\n",
      "Epoch 263/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1282 - acc: 0.9743 - val_loss: 0.1615 - val_acc: 0.9533\n",
      "Epoch 264/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1273 - acc: 0.9771 - val_loss: 0.1607 - val_acc: 0.9533\n",
      "Epoch 265/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1270 - acc: 0.9771 - val_loss: 0.1598 - val_acc: 0.9533\n",
      "Epoch 266/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1267 - acc: 0.9771 - val_loss: 0.1595 - val_acc: 0.9533\n",
      "Epoch 267/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1258 - acc: 0.9771 - val_loss: 0.1595 - val_acc: 0.9533\n",
      "Epoch 268/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1252 - acc: 0.9771 - val_loss: 0.1591 - val_acc: 0.9533\n",
      "Epoch 269/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1245 - acc: 0.9771 - val_loss: 0.1589 - val_acc: 0.9533\n",
      "Epoch 270/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1241 - acc: 0.9800 - val_loss: 0.1583 - val_acc: 0.9533\n",
      "Epoch 271/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1236 - acc: 0.9771 - val_loss: 0.1581 - val_acc: 0.9533\n",
      "Epoch 272/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.1231 - acc: 0.9771 - val_loss: 0.1580 - val_acc: 0.9533\n",
      "Epoch 273/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1225 - acc: 0.9800 - val_loss: 0.1574 - val_acc: 0.9533\n",
      "Epoch 274/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1221 - acc: 0.9800 - val_loss: 0.1567 - val_acc: 0.9533\n",
      "Epoch 275/300\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.1217 - acc: 0.9771 - val_loss: 0.1565 - val_acc: 0.9533\n",
      "Epoch 276/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1211 - acc: 0.9771 - val_loss: 0.1562 - val_acc: 0.9533\n",
      "Epoch 277/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1205 - acc: 0.9800 - val_loss: 0.1559 - val_acc: 0.9533\n",
      "Epoch 278/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1204 - acc: 0.9800 - val_loss: 0.1556 - val_acc: 0.9533\n",
      "Epoch 279/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1195 - acc: 0.9800 - val_loss: 0.1552 - val_acc: 0.9533\n",
      "Epoch 280/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1191 - acc: 0.9800 - val_loss: 0.1545 - val_acc: 0.9533\n",
      "Epoch 281/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1189 - acc: 0.9800 - val_loss: 0.1541 - val_acc: 0.9533\n",
      "Epoch 282/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1185 - acc: 0.9771 - val_loss: 0.1545 - val_acc: 0.9533\n",
      "Epoch 283/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1179 - acc: 0.9800 - val_loss: 0.1539 - val_acc: 0.9533\n",
      "Epoch 284/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1175 - acc: 0.9800 - val_loss: 0.1540 - val_acc: 0.9533\n",
      "Epoch 285/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1168 - acc: 0.9800 - val_loss: 0.1531 - val_acc: 0.9533\n",
      "Epoch 286/300\n",
      "350/350 [==============================] - 0s 64us/step - loss: 0.1166 - acc: 0.9800 - val_loss: 0.1525 - val_acc: 0.9533\n",
      "Epoch 287/300\n",
      "350/350 [==============================] - 0s 64us/step - loss: 0.1163 - acc: 0.9800 - val_loss: 0.1524 - val_acc: 0.9533\n",
      "Epoch 288/300\n",
      "350/350 [==============================] - 0s 64us/step - loss: 0.1156 - acc: 0.9800 - val_loss: 0.1519 - val_acc: 0.9533\n",
      "Epoch 289/300\n",
      "350/350 [==============================] - 0s 70us/step - loss: 0.1152 - acc: 0.9800 - val_loss: 0.1520 - val_acc: 0.9533\n",
      "Epoch 290/300\n",
      "350/350 [==============================] - 0s 76us/step - loss: 0.1150 - acc: 0.9800 - val_loss: 0.1513 - val_acc: 0.9533\n",
      "Epoch 291/300\n",
      "350/350 [==============================] - 0s 68us/step - loss: 0.1143 - acc: 0.9771 - val_loss: 0.1511 - val_acc: 0.9533\n",
      "Epoch 292/300\n",
      "350/350 [==============================] - ETA: 0s - loss: 0.1343 - acc: 0.968 - 0s 68us/step - loss: 0.1138 - acc: 0.9771 - val_loss: 0.1513 - val_acc: 0.9533\n",
      "Epoch 293/300\n",
      "350/350 [==============================] - 0s 69us/step - loss: 0.1135 - acc: 0.9800 - val_loss: 0.1500 - val_acc: 0.9533\n",
      "Epoch 294/300\n",
      "350/350 [==============================] - 0s 69us/step - loss: 0.1131 - acc: 0.9771 - val_loss: 0.1500 - val_acc: 0.9533\n",
      "Epoch 295/300\n",
      "350/350 [==============================] - 0s 66us/step - loss: 0.1126 - acc: 0.9771 - val_loss: 0.1503 - val_acc: 0.9533\n",
      "Epoch 296/300\n",
      "350/350 [==============================] - 0s 67us/step - loss: 0.1123 - acc: 0.9800 - val_loss: 0.1497 - val_acc: 0.9533\n",
      "Epoch 297/300\n",
      "350/350 [==============================] - 0s 69us/step - loss: 0.1119 - acc: 0.9771 - val_loss: 0.1490 - val_acc: 0.9533\n",
      "Epoch 298/300\n",
      "350/350 [==============================] - 0s 65us/step - loss: 0.1117 - acc: 0.9771 - val_loss: 0.1489 - val_acc: 0.9533\n",
      "Epoch 299/300\n",
      "350/350 [==============================] - 0s 69us/step - loss: 0.1111 - acc: 0.9800 - val_loss: 0.1490 - val_acc: 0.9533\n",
      "Epoch 300/300\n",
      "350/350 [==============================] - 0s 68us/step - loss: 0.1108 - acc: 0.9771 - val_loss: 0.1486 - val_acc: 0.9533\n"
     ]
    }
   ],
   "source": [
    "# Instantiating the model\n",
    "model = a_simple_NN()\n",
    "\n",
    "# Splitting the dataset into training (70%) and validation sets (30%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features, labels, test_size=0.3)\n",
    "\n",
    "# Setting the number of passes through the entire training set\n",
    "num_epochs = 300\n",
    "\n",
    "# model.fit() is used to train the model\n",
    "# We can pass validation data while training\n",
    "model_run = model.fit(X_train, y_train, epochs=num_epochs,\n",
    "                      validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><p><i class=\"fa fa-info-circle\"></i>&nbsp;\n",
    "    NOTE: We can pass \"verbose=0\" to model.fit() to suppress the printing of model output on the terminal/notebook.\n",
    "</p></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the loss and accuracy on the training and validation sets during the training\n",
    "# This can be done by using Keras callback \"history\" which is applied by default\n",
    "history_model = model_run.history\n",
    "\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
    "plt.xlabel(\"epochs\") ;\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "ReLU is very popular and is widely used nowadays. There also exist other variations of ReLU, e.g. \"leaky ReLU\".\n",
    "</p>\n",
    "</div>plt.ylabel(\"accuracy\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "The plots such as above are very important for analyzing the behaviour and performance of the network and to tune it in the right direction. However, for the example above we don't really expect to derive a lot of insight from this plot as the function we are trying to fit is quiet simple and there is not too much noise. We will see the significance of these curves in a later example.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above we splitted our dataset into a 70-30 train-validation set. We know from previous chapters that to more robustly calculate accuracy we can use **K-fold crossvalidation**.\n",
    "This is even more important when we have small datasets and cannot afford to reserve a validation set!\n",
    "\n",
    "One way to do the cross validation here would be to write our own function to do this. However, we also know that **SciKit learn** provides several handy functions to evaluate and tune the models. So the question is:\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "    Can we somehow use the Scikit learn functions or ones we wrote ourselves for Scikit learn models to evaluate and tune our Keras models?\n",
    "\n",
    "\n",
    "The Answer is **YES !**\n",
    "</p>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "We show how to do this in the following section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using SciKit learn functions on Keras models\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "Keras offers 2 wrappers which allow its Sequential models to be used with SciKit learn. \n",
    "\n",
    "There are: **KerasClassifier** and **KerasRegressor**.\n",
    "\n",
    "For more information:\n",
    "https://keras.io/scikit-learn-api/\n",
    "</p>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "**Now lets see how this works!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We wrap the Keras model we created above with KerasClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# Wrapping Keras model\n",
    "# NOTE: We pass verbose=0 to suppress the model output\n",
    "num_epochs = 400\n",
    "model_scikit = KerasClassifier(\n",
    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's reuse the function to visualize the decision boundary which we saw in chapter 2 with minimal change\n",
    "\n",
    "def list_flatten(list_of_list):\n",
    "    flattened_list = [i for j in list_of_list for i in j]\n",
    "    return flattened_list\n",
    "\n",
    "def plot_points(plt=plt, marker='o'):\n",
    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
    "\n",
    "def train_and_plot_decision_surface(\n",
    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
    "):\n",
    "\n",
    "    features_2d = np.array(features_2d)\n",
    "    xmin, ymin = features_2d.min(axis=0)\n",
    "    xmax, ymax = features_2d.max(axis=0)\n",
    "\n",
    "    x = np.linspace(xmin, xmax, N)\n",
    "    y = np.linspace(ymin, ymax, N)\n",
    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
    "\n",
    "    if preproc is not None:\n",
    "        points_for_classifier = preproc.fit_transform(points)\n",
    "        features_2d = preproc.fit_transform(features_2d)\n",
    "    else:\n",
    "        points_for_classifier = points\n",
    "\n",
    "    classifier.fit(features_2d, labels, verbose=0)\n",
    "    predicted = classifier.predict(features_2d)\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        predicted = list_flatten(predicted)\n",
    "    \n",
    "    \n",
    "    if preproc is not None:\n",
    "        name += \" (w/ preprocessing)\"\n",
    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
    "    else:\n",
    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
    "    plt.plot(\n",
    "        points[~classes][:, 0],\n",
    "        points[~classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"steelblue\",\n",
    "        markersize=1,\n",
    "        alpha=0.01,\n",
    "    )\n",
    "    plt.plot(\n",
    "        points[classes][:, 0],\n",
    "        points[classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"chocolate\",\n",
    "        markersize=1,\n",
    "        alpha=0.04,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net:\t 486 / 500 correct\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 363,
       "width": 383
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
    "plot_points(plt=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The acuracy on the  5  validation folds: [0.97 0.95 0.96 0.97 0.96]\n",
      "The Average acuracy on the  5  validation folds: 0.962\n"
     ]
    }
   ],
   "source": [
    "# Applying K-fold cross-validation\n",
    "# Here we pass the whole dataset, i.e. features and labels, instead of splitting it.\n",
    "num_folds = 5\n",
    "cross_validation = cross_val_score(\n",
    "    model_scikit, features, labels, cv=num_folds, verbose=0)\n",
    "\n",
    "print(\"The acuracy on the \", num_folds, \" validation folds:\", cross_validation)\n",
    "print(\"The Average acuracy on the \", num_folds, \" validation folds:\", np.mean(cross_validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE: The above code took quiet long even though we used only 5  CV folds and the neural network and data size are very small!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know from chapter 6 that there are 2 types of parameters which need to be tuned for a machine learning model.\n",
    "* Internal model parameters (weights) which can be learned for e.g. by gradient-descent\n",
    "* Hyperparameters\n",
    "\n",
    "In the model which we created above we made some arbitrary choices like which optimizer we use, what is its learning rate, number of hidden units and so on ...\n",
    "\n",
    "Now that we have the keras model wrapped as a scikit model we can use the grid search functions we have seen in chapter 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# Just to remember\n",
    "model_scikit = KerasClassifier(\n",
    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.894 {'epochs': 300}\n"
     ]
    }
   ],